Variance filtering prior to RNA-seq differential expression
1
0
Entering edit mode
chris86 ▴ 420
@chris86-8408
Last seen 5.0 years ago
UCL, United Kingdom

Hi

I want to variance filter my RNA-seq data to increase statistical power similar to what I have seen done in microarray studies where the data groups are pretty similar. However I cannot use limma/deseq etc after the variance filter because it screws up the background variance, is it OK to do a t-test? I think there are problems with the normal distribution not fitting RNA-seq data, in which case, what to do? Any ideas?

Cheers.

sequencing deseq limma • 3.8k views
ADD COMMENT
3
Entering edit mode
@gordon-smyth
Last seen 2 minutes ago
WEHI, Melbourne, Australia

Don't do variance filtering -- then the problems you mention will all disappear.

Variance filtering is hardly ever a good idea, and almost certainly not with RNA-seq data.

ADD COMMENT
0
Entering edit mode

I' new to rna seq analysis. So pardon my ignorance.

Why is variance filtering hardly ever a good idea, and almost certainly not with RNA-seq data?

What if your downstream analysis is a clustering algorithm instead of differential expression?

 

Best

ADD REPLY
0
Entering edit mode

For clustering that is fine, it makes sense. For differential expression many of the packages do variance sharing to increase power so you cannot remove the low variance ones. Actually I compared variance filtering + t test with limma for finding DE genes, and limma came out on top because it does this information sharing.

ADD REPLY
0
Entering edit mode

Oh cool. Thanks for the info!

ADD REPLY

Login before adding your answer.

Traffic: 744 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6